New Frontiers of Robo-Advising: Consumption, Saving, Debt Management, and Taxes

Abstract

Traditional forms of robo-advice were targeted to help individuals make portfolio allocation decisions. Based on the balance-sheet view of households, the scope for robo-advising has been expanding to many other personal-finance choices, such as households' saving and consumption decisions, debt management, mortgage uptake, tax management, and lending. This chapter reviews existing research on these new functions of robo-advising with a special emphasis on the questions that are still open for researchers across several disciplines. We also discuss the attempts to optimize jointly all personal-finance decisions, which we label ``holistic robo-advisors.'' We conclude by assessing fruitful avenues for research and practice in finance, computer science, marketing, decision science, information systems, law, and sociology.

Keywords: Algorithmic Advice, FinTech, Income Aggregators, Peer-to-Peer Lending, Financial Apps, Financial Regulation, Machine Learning Applications to Finance, TechFin, Disclosure, Credit Cards, Financial Regulation, Financial Literacy, Household Finance, Personal Finance, Trust, Financial Advisors, Peer

JEL Classification: D14, G21